Loot boxes are a popular mechanic within many video games, but it remains unclear if some forms of loot boxes can be seen of as gambling. However, the perspectives of players are often neglected, such as whether they see them as ‘fair’ game elements and how closely they feel this aligns with gambling. In this paper, we synthesise a conceptualisation for loot boxes through players’ actual experience and explore if there are any parallels with gambling. Twenty-one participants who played video games took part in the research through either an interview or online survey. Thematic analysis suggested that six themes were core to exploring loot boxes: Random Chance Effects, Attitudes Towards Content, Implementation, Parallels with Gambling, Game Design, and The Player. The results suggested both indirect and direct parallels with gambling from the players experiences. Implications of game design and classifying loot boxes as gambling are discussed in relation to game design and risk factors of gambling and purchasing behaviour.
Motivation to engage in learning is essential for learning performance. Learners’ motivation is traditionally assessed using self-reported data, which is time-consuming, subjective, and interruptive to their learning process. To address this issue, this paper proposes a novel framework for multimodal assessment of learners’ motivation in e-learning environments with the ultimate purpose of supporting intelligent e-learning systems to facilitate dynamic, context-aware, and personalized services or interventions, thus sustaining learners’ motivation for learning engagement. We investigated the performance of the machine learning classifier and the most and least accurately predicted motivational factors. We also assessed the contribution of different electroencephalogram (EEG) and eye gaze features to motivation assessment. The applicability of the framework was evaluated in an empirical study in which we combined eye tracking and EEG sensors to produce a multimodal dataset. The dataset was then processed and used to develop a machine learning classifier for motivation assessment by predicting the levels of a range of motivational factors, which represented the multiple dimensions of motivation. We also proposed a novel approach to feature selection combining data-driven and knowledge-driven methods to train the machine learning classifier for motivation assessment, which has been proved effective in our empirical study at selecting predictors from a large number of extracted features from EEG and eye tracking data. Our study has revealed valuable insights for the role played by brain activities and eye movements on predicting the levels of different motivational factors. Initial results using logistic regression classifier have achieved significant predictive power for all the motivational factors studied, with accuracy of between 68.1% and 92.8%. The present work has demonstrated the applicability of the proposed framework for multimodal motivation assessment which will inspire future research towards motivationally intelligent e-learning systems.
IntroductionGambling is increasingly recognised as an important public health issue. Problem gambling is associated with highly negative impacts on physical, psychological and social well-being, not only for those who gamble but also for those around them. There has been a rapid expansion of internet gambling and attributes such as continuous play and instant rewards, and enhanced privacy may lead to a greater likelihood of gambling-related harms. In this randomised controlled feasibility study, we are testing (1) the acceptability and feasibility of three online responsible gambling interventions targeting people with low-to-moderate risk of online problem gambling and (2) the feasibility of a future full-scale randomised controlled trial (RCT) to test their effectiveness and cost-effectiveness.Methods and analysisFour-arm randomised controlled feasibility study with qualitative substudy. One-hundred and forty UK residents with low-to-moderate risk of online gambling recruited via gambling operators and social media will be randomised (1:1:1:1) to either (1) goal setting, (2) descriptive norms messages (challenge perceptions of peer behaviours), (3) injunctive norms messages (challenge perceptions of peer attitudes) and (4) control (delayed intervention). Interventions will be delivered over 6 weeks and individually tailored. Outcomes, administered online, will be measured at baseline, 7 weeks, and 3 and 6 months post randomisation (including gambling risk behaviours and cognitions, anxiety and depression, quality of life, health use and productivity). Analyses will be descriptive, focusing on feasibility and acceptability of the interventions and study procedures. Telephone/online interviews, with a subsample of approximately 30 participants, will elicit experiences of participating in the study. Prespecified progression criteria will guide decisions around whether to progress to a definitive RCT.Ethics and disseminationEthical approval obtained from Bournemouth University Research Ethics Committee (reference number 33247). Participants will be given a participant information sheet plus a ‘Key Facts’ summary and will provide informed online consent. Findings will be published in peer-reviewed journals and presented at conferences and public engagement events.Trial registration numberISRCTN37874344.
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